Stefanie Rinderle‐Ma

Technical University of Munich, University of Vienna

Papers

7

Total Citations

121

H-Index

6

About

Stefanie Rinderle-Ma is a prominent researcher whose work sits at the intersection of process automation, process mining, and human-robot collaboration in manufacturing environments. Her research addresses critical challenges in modernizing industrial processes through intelligent automation and data-driven techniques. Among her most significant contributions is pioneering work on Interactive Process Automation, which leverages lightweight object detection to facilitate seamless human-cobot interaction in manufacturing settings such as picking and placing scenarios. This work is complemented by her investigations into the cobot assignment and job shop scheduling problem, where she applies sophisticated metaheuristic approaches—including biased random-key genetic algorithms and hybrid metaheuristics—to optimize collaborative robot deployment alongside human workers, collectively accumulating over 70 citations across related publications. Rinderle-Ma has also advanced the application of process mining in manufacturing, developing conformance checking techniques to bridge the gap between shop floor data collection and orchestration software. Her work on cognition-aware safety systems further demonstrates her commitment to sustainable, adaptive human-machine interaction beyond rigid traditional certification frameworks. Her research consistently addresses real-world industrial challenges, making meaningful contributions to the fields of intelligent manufacturing, process intelligence, and human-centered automation that resonate strongly with both academic researchers and industry practitioners.

Research Focus

Key Achievements

6
H-Index
7
Papers
121
Total Citations
17
Avg Citations/Paper
🏆 Most Cited Paper
Interactive Process Automation based on lightweight object detection in manufacturing processes
26 citations · 2021
📈 Most Prolific Year: 2021 (4 Papers)
🤝 Key Collaborators: 16
🏛 Institutions: Technical University of Munich, University of Vienna

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 15 days ago